Management of isolated distal deep vein thrombosis (IDDVT) remains controversial. We summarize recent studies regarding the natural history of IDDVT as well as pertinent therapeutic trials. We also provide our management approach.
IDDVT is more commonly associated with transient risk factors and less often associated with permanent, unmodifiable risk factors than proximal DVT. IDDVT has a significantly lower risk of proximal extension and recurrence than proximal DVT. Cancer-associated IDDVT has a similar natural history to cancer-associated proximal DVT, with substantially less favourable outcomes than noncancer-associated IDDVT. Anticoagulant treatment reduces the risk of proximal extension and recurrence in IDDVT at the cost of increased bleeding risk. Intermediate dosing of anticoagulation may be effective for treating noncancer-associated IDDVT in patients without prior DVT.
IDDVT with a transient risk factor can be treated for 6?weeks in patients without a prior DVT. Unprovoked IDDVT in patients without malignancy can be treated for 3?months. Outpatients without malignancy or a prior DVT can be left untreated and undergo surveillance compression ultrasound in one week to detect proximal extension, but few patients opt for this in practice. Cancer-associated IDDVT should be treated analogously to cancer-associated proximal DVT.
IDDVT with a transient risk factor can be treated for 6?weeks in patients without a prior DVT. Unprovoked IDDVT in patients without malignancy can be treated for 3?months. Outpatients without malignancy or a prior DVT can be left untreated and undergo surveillance compression ultrasound in one week to detect proximal extension, but few patients opt for this in practice. Cancer-associated IDDVT should be treated analogously to cancer-associated proximal DVT.Patients, surrogate decision makers, and clinicians face weighty and urgent decisions under uncertainty in the ICU, which could be aided by risk prediction. Although emerging artificial intelligence/machine learning (AI/ML) algorithms could reduce uncertainty surrounding these life and death decisions, certain criteria must be met to ensure their bedside value.
Although ICU severity of illness scores have existed for decades, these tools have not been shown to predict well or to improve outcomes for individual patients. Novel AI/ML tools offer the promise of personalized ICU care but remain untested in clinical trials. Ensuring that these predictive models account for heterogeneity in patient characteristics and treatments, are not only specific to a clinical action but also consider the longitudinal course of critical illness, and address patient-centered outcomes related to equity, transparency, and shared decision-making will increase the likelihood that these tools improve outcomes. Improved clarity around standards and contributions from institutions and critical care departments will be essential.
Improved ICU prognostication, enabled by advanced ML/AI methods, offer a promising approach to inform difficult and urgent decisions under uncertainty. However, critical knowledge gaps around performance, equity, safety, and effectiveness must be filled and prospective, randomized testing of predictive interventions are still needed.
Improved ICU prognostication, enabled by advanced ML/AI methods, offer a promising approach to inform difficult and urgent decisions under uncertainty. However, critical knowledge gaps around performance, equity, safety, and effectiveness must be filled and prospective, randomized testing of predictive interventions are still needed.Intensive care unit (ICU) survivorship has gained significant attention over the course of the COVID-19 pandemic. In this review, we summarize the contemporary literature in relation to the epidemiology and management of post-ICU problems.
Survivors of critical illness can have complex physical, social, emotional and cognitive needs in the months following hospital discharge. Emerging evidence has shown that pre-ICU characteristics such as educational attainment, alongside in-ICU factors such as delirium, may contribute to worsening outcomes. Evidence regarding the impact of post-ICU recovery services is evolving, but models such as post-ICU clinics and peer support programs are gaining rapid momentum.
Future research should focus on modifiable risk factors and how identification and treatment of these can improve outcomes. Furthermore, rigorous evaluation of postacute critical care recovery services is necessary.
Future research should focus on modifiable risk factors and how identification and treatment of these can improve outcomes. https://www.selleckchem.com/products/tj-m2010-5.html Furthermore, rigorous evaluation of postacute critical care recovery services is necessary.Resource limitation, or capacity strain, has been associated with changes in care delivery, and in some cases, poorer outcomes among critically ill patients. This may result from normal variation in strain on available resources, chronic strain in persistently under-resourced settings, and less commonly because of acute surges in demand, as seen during the coronavirus disease 2019 (COVID-19) pandemic.
Recent studies confirmed existing evidence that high ICU strain is associated with ICU triage decisions, and that ICU strain may be associated with ICU patient mortality. Studies also demonstrated earlier discharge of ICU patients during high strain, suggesting that strain may promote patient flow efficiency. Several studies of strain resulting from the COVID-19 pandemic provided support for the concept of adaptability - that the surge not only caused detrimental strain but also provided experience with a novel disease entity such that outcomes improved over time. Chronically resource-limited settings faced even more challenging circumstances because of acute-on-chronic strain during the pandemic.
The interaction between resource limitation and care delivery and outcomes is complex and incompletely understood. The COVID-19 pandemic provides a learning opportunity for strain response during both pandemic and nonpandemic times.
The interaction between resource limitation and care delivery and outcomes is complex and incompletely understood. The COVID-19 pandemic provides a learning opportunity for strain response during both pandemic and nonpandemic times.